• International journal of advanced smart convergence
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• 제8권4호
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• IEIE Transactions on Smart Processing and Computing
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• 제3권4호
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• pp.226-233
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• 2014

This paper presents an external light evasion method that rectifies the problem of misrecognition due to external lighting. The fundamental concept underlying the proposed method involves recognition of the differences between two images and elimination of the desynchronized external light by synchronizing the image sensor and inner light source of the optical touch screen. A range of artificial indoor light sources and natural sunlight are assessed. The proposed system synchronizes with a Vertical Synchronization (VSYNC) signal and the light source drive signal of the image sensor. Therefore, it can display synchronized light of the acquired image through the image sensor and remove external light that is not from the light source. A subtraction operation is used to find the differences and the absolute value of the result is utilized; hence, the order is irrelevant. The resulting image, which displays only a touched blob on the touchscreen, was created after image processing for coordination recognition and was then supplied to a coordination extraction algorithm.

• 한국전자통신학회논문지
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• 제11권11호
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• pp.1093-1100
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• 2016

본 연구는 슈퍼캐패시티 EDLC(:Electric double layer capacitor)를 적용한 가로등용 태양광-풍력 하이브리드 발전시스템 성능의 우수함과 안정성을 입증하고자 한다. 독립전원용 태양광-풍력 하이브리드 발전시스템의 부하로 LED 광원을 이용한 조명장치를 적용하고 고출력 발전시스템을 통한 가로등 시스템 장치의 개발을 목표로 하고 있다. 발전된 전력을 배터리에 저장하는데 있어서 보조장치로 적용될 EDLC는 기존의 컨트롤러와는 달리 배터리의 고출력과 긴 수명을 보장할 수 있어 기존장치에 비해 유지보수가 적게 들고 우수한 성능을 확보할 수 있다.

• IEIE Transactions on Smart Processing and Computing
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• 제2권4호
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• pp.248-254
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• 2013

The size and power consumption of digital circuits including the dimming circuit part will increase for high-performance solid state lighting (SSL) systems in the future. This study examined the low-power consumption of adiabatic dynamic CMOS logic (ADCL) due to the principles of adiabatic charging. Furthermore, the designed low-power ADCL digital pulse width modulation (PWM) was optimized for SSL dimming systems. For this purpose, an ADCL digital 3-bit PWM was optimized in two steps. In the first step, the architecture of the ADCL digital 3-bit PWM was miniaturized. In the second step, the clock cut-off circuit was designed and added to the ADCL PWM. As a result, compared to the original configuration, 60 transistors and 15 capacitors of ADCL digital 3-bit PWM were reduced for miniaturization. Moreover, the clock cut-off circuit, which controls wake-up and sleep mode of ADCL D-FFs, was designed. The power consumption of an optimized ADCL digital PWM for all bit patterns decreased by 54 %.

• 국제초고층학회논문집
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• 제6권4호
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• pp.297-300
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• 2017

Employers are realizing that workplaces have an immense impact on productivity. And, because people typically represent about 90% of a company's costs, even a slight improvement in productivity hits the bottom-line in a very big way. The focus is shifting away from saving money by reducing space needs or cutting energy requirements, and moving toward making the workplace friendlier and healthier. What is a healthy workplace, exactly? The answer should be clear by now. Mountains of research have expounded on this very topic, and then news outlets cover the latest discoveres ad infinitum. The problem is, research is rather dense, while the news isn't comprehensive. Office managers trying to improve workplace wellness want neither a 55-page report nor a half dozen articles to convince their landlord that their space can do better. Recognizing this gap, Harvard researchers have released a concise-yet-complete list of healthy building features. Here, we're sharing these nine features with a few insights of our own on the value of being healthy and productive. Find out: ${\bullet}$ What makes a healthy building and why is it so important ${\bullet}$ How to improve the health of your own office ${\bullet}$ What happens for your people and your bottom line when you make health a priority.

• Smart Structures and Systems
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• 제24권6호
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• 교육시설 논문지
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• 제26권3호
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• pp.33-41
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• 2019

As the information age matures, the learning style of youth is changing rapidly. Students study at a variety of places such as cafe or lobbies utilizing various digital learning devices. Along with the place changes, learning methods are also changing. Student-centered learning methods such as smart learning, collaborative learning, and activity-based learning are increasingly being utilized instead of the traditional instructor-centered learning in which knowledge is unilaterally delivered. Accordingly, many universities are remodeling central libraries, and they are also transforming lobby spaces of the college buildings into simple but useful learning spaces. In this study, we analyze the characteristics of learning spaces in universities from the standpoint of the students. According to the analysis, overall satisfaction is high in terms of comfortable physical learning environments such as Wi-Fi, furniture, lighting, etc. But the spaces are still optimized for individual and intensive learning. There seems to be a lack of effort to support collaborative learning or activity-based learning. This observation is confirmed by the characteristics of the central library, and it is considered that the reason why the college buildings are preferred by students is that college buildings are more suitable for collaborative or activity-based learning than libraries.

• 한국인터넷방송통신학회논문지
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• 제20권6호
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• pp.71-76
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• 2020

최근에 독서실 이용의 증가로 독서실 시장이 계속 성장하고 있다. 그러나 기존 독서실 업체들은 청결 문제와 도난 사고가 빈번하게 발생하고, 이를 실시간으로 처리할 수 없는 문제가 있다. 또한, 독서실에서는 조명을 사용하는데, 사용자가 조명을 킨 상태로 오랜 시간 자리를 비워, 전력 낭비가 심한 문제도 있다. 그리하여 본 논문에서는 아두이노를 통해 도난사고를 방지하고 자동으로 책상을 청소해주며 사용자의 상태에 따라 조명 밝기를 조정할 수 있는 서비스와 안드로이드를 통해 사물을 통해 얻은 정보를 기반으로 분실물이 발생하거나 다른 사용자가 해당 책상에 접근하는 경우 단말기에 알림 전송할 수 있는 시스템을 설계하고 구현하고자 한다.

• 한국전기전자재료학회논문지
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• 제37권1호
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• pp.1-10
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• 2024

Direct use of sunlight through the glass windows is an efficient way to reduce the energy consumption related to the heating, cooling, and lighting. Introduction of near-infrared modulating properties through colloidal doped metal oxide nanocrystals into the classical electrochromic materials accelerates the development of next-generation electrochromic devices. There has been a steady enhancement in the performance of electrochromic devices, necessitating a review of the recent progress in next-generation electrochromic devices employing doped metal oxide nanocrystals. This review provides an overview of the current developments in next-generation electrochromic smart windows utilizing colloidal doped metal oxide nanocrystals, with a focus on the key factors for achieving these advanced windows. Colloidal doped metal oxide nanocrystals are a crucial component in realizing and bringing to market the next generation of electrochromic windows, though further research and development are still required in this regard.

• 센서학회지
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• 제33권3호
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• pp.179-184
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• 2024

In the car industry, welding is a fundamental linking technique used for joining components, such as steel, molds, and automobile parts. However, accurate inspection is required to test the reliability of the welding components. In this study, we investigate the detection of weld beads using 2D image processing in an automatic recognition system. The sample image is obtained using a 2D vision camera embedded in a lighting system, from where a portion of the bead is successfully extracted after image processing. In this process, the soot removal algorithm plays an important role in accurate weld bead detection, and adopts adaptive local gamma correction and gray color coordinates. Using this automatic recognition system, geometric parameters of the weld bead, such as its length, width, angle, and defect size can also be defined. Finally, on comparing the obtained data with the industrial standards, we can determine whether the weld bead is at an acceptable level or not.